Systems and methods for interference management in a radio access network

ABSTRACT

A system described herein may provide for the use of artificial intelligence/machine learning (“AI/ML”) techniques to generate models for various locations or regions (e.g., sectors) associated with one or more radio access networks (“RANs”) of a wireless network. The system may further use AI/ML techniques to generate interference models to reflect types and/or amounts of radio frequency (“RF”) interference measured within the RAN. The system may further determine, based on received RF metrics for a given sector, a particular interference model associated with the sector. Based on a sector model associated with the sector and the determined interference model, one or more actions may be determined in order to remediate any potential interference associated with the sector or surrounding sectors.

CROSS-REFERENCE TO RELATED APPLICATION

This Application is a Continuation of U.S. patent application Ser. No.17/138,483, filed on Dec. 30, 2020, titled “SYSTEMS AND METHODS FORINTERFERENCE MANAGEMENT IN A RADIO ACCESS NETWORK,” the contents ofwhich are herein incorporated by reference in their entirety.

BACKGROUND

Wireless networks, such as Long-Term Evolution (“LTE”) networks, FifthGeneration (“5G”) networks, or the like, may include radio accessnetworks (“RANs”), via which user equipment (“UE”), such as mobiletelephones or other wireless communication devices, may receive wirelessservice. Diverse geographical regions of a RAN may be served bydifferent sets of infrastructure hardware, which may cause interferencewithin the RAN in some scenarios.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 illustrates an example overview of one or more embodimentsdescribed herein;

FIG. 2 illustrates example interference models, sector models, and/oractions/parameters that may be generated, received, maintained,provided, etc. by a Global Optimization System (“GOS”) of someembodiments;

FIG. 3 illustrates example attributes associated with a particularsector model, in accordance with some embodiments;

FIGS. 4A-4C illustrate an example determination of an interference modelassociated with a given sector, in accordance with some embodiments;

FIG. 5 illustrates an example of an interference model associated with agiven sector, in accordance with some embodiments;

FIG. 6 illustrates an example of one or more sector models associatedwith a given sector associated with a RAN of a wireless network, inaccordance with some embodiments;

FIGS. 7A-7C illustrate example remedial actions that may be taken basedon interference models determined with respect to one or more sectors,in accordance with some embodiments;

FIG. 8 illustrates an example process for determining one or more sectormodels, interference models, and/or sets of actions to perform withrespect to a given sector associated with a RAN of a wireless network,in accordance with some embodiments;

FIG. 9 illustrates an example environment in which one or moreembodiments, described herein, may be implemented;

FIG. 10 illustrates an example arrangement of a radio access network(“RAN”), in accordance with some embodiments;

FIG. 11 illustrates an example arrangement of an Open RAN (“O-RAN”)environment in which one or more embodiments, described herein, may beimplemented; and

FIG. 12 illustrates example components of one or more devices, inaccordance with one or more embodiments described herein.

DETAILED DESCRIPTION OF EXAMPLE EMBODIMENTS

The following detailed description refers to the accompanying drawings.The same reference numbers in different drawings may identify the sameor similar elements.

Embodiments described herein provide for the use of artificialintelligence/machine learning (“AI/ML”) techniques or other suitabletechniques to model attributes, characteristics, key performanceindicators (“KPIs”), and/or other information associated with variouslocations or regions associated with one or more RANs of a wirelessnetwork (e.g., a LTE network, a 5G network, and/or another type ofnetwork). As discussed herein, such locations or regions may be referredto as “sectors.” Further, in the examples discussed herein, sectors mayinclude evenly distributed areas of a uniform shape (e.g., a hexagon).In practice, sectors may be arranged or defined differently. Forexample, in some embodiments, sectors may be defined with respect to thelocation of one or more base stations of a RAN (e.g., where a sector maybe defined based on a coverage area of the one or more base stationsand/or may be defined based on a physical location at which one or moreantennas or other physical equipment of the base stations areinstalled), and/or may be defined independently of the location of theone or more base stations.

Embodiments described herein further provide for the use of AI/MLtechniques or other suitable techniques to determine measures of radiofrequency (“RF”) channel quality, interference, and/or other attributesof particular sectors, as well as the determination of suitable remedialand/or performance-enhancing actions (referred to sometimes hereinsimply as “actions”) to perform to enhance RF channel quality, reduceinterference, and/or otherwise enhance the operation of the RAN. Someembodiments may select or perform actions (or sets of actions) based onparticular characteristics (e.g., which may be reflected by sectormodels) of particular sectors. Thus, as discussed below, differentactions may be performed for sectors that experience similar types ofinterference, but which are associated with different sector models.

Interference and/or degraded RF channel quality may be caused by, forexample, multiple network infrastructure devices, such as base stations,operating in relatively close proximity. Signals may “collide,” such assignals that are output by such base station on the same frequencyand/or time domain. For example, multiple base stations may broadcast,or otherwise output, reference signals on a control channel (e.g., aPhysical Downlink Control Channel (“PDCCH”), a Physical Downlink SharedChannel (“PDSCH”), or the like). The reference signals may includetiming information, frequency information, cell identifier information,and/or other suitable information based on which a UE may detect thepresence of, and/or connect to, a given base station. In someembodiments, the reference signals may include a Demodulation ReferenceSignal (“DMRS”), a Phase Tracking Reference Signal (“PTRS”), a SoundingReference Signal (“SRS”), a Channel State Information Reference Signal(“CSI-RS”), a pilot signal, and/or some other suitable type of referencesignal.

In some scenarios, the reference signals may be output at a higher powerthan “user plane” data (e.g., voice call traffic, data traffic, or thelike, wirelessly sent to UEs from base stations). As such, scenarios mayarise where “control plane” traffic (e.g., reference signals) frommultiple base stations may collide, thus resulting in the detection ofinterference at a UE that receives or detects the control plane trafficfrom multiple base stations. Additionally, or alternatively, controlplane traffic from one base station may interfere with user planetraffic from another base station. As another example, interference maybe caused by “unknown” interference sources, such as RF-emitting devicesnot necessarily owned and/or operated by a same entity as an ownerand/or operator of the RAN. Such RF-emitting devices may include drones,weather measurement devices, and/or other types of devices that emit RFsignals and/or otherwise cause RF interference.

Since embodiments described herein include the determination andperformance of actions in a dynamic manner that is based on theparticular characteristics of sectors at which interference is detected,the levels of interference in the RAN may be reduced, thereforeenhancing the user experience of users using UEs that receive wirelessservice from the RAN, as well as enhancing the overall operation of theRAN itself. Further, as described herein, the association betweenparticular sector models, interference models, and/or associated actionsmay be generated and/or refined using one or more AI/ML techniques orother suitable techniques (e.g., deep learning, reinforced orunreinforced machine learning, neural networks, K-means clustering,regression analysis, and/or other suitable techniques). In this manner,the particular characteristics of a sector may be taken into accountwhen selecting actions to perform in order to enhance the performance(e.g., increased RF channel quality, reduced interference, etc.) of aRAN, while reducing or eliminating the need for manual intervention inorder to determine or implement such actions.

As shown in FIG. 1 , for example, geographical area (or region) 100 maybe subdivided into a set of sectors 101. The set of sectors 101 mayinclude, as shown, sector 101-1, 101-2, and one or more additionalsectors that are not explicitly illustrated with a reference numeral.Further in this example, each sector 101 may be associated with discretenetwork infrastructure elements, such as particular base stations 103.For example, base station 103-1 may be located in one particular sector101, while base station 103-2 may be located in another sector 101.Further, additional base stations 103 (e.g., base stations notexplicitly illustrated with a reference numeral) may be present ingeographical region 100. That is, the location of each base station 103may be within a particular geographical area (e.g., a hexagonal-shapedgeographical area, in this example) that corresponds to a respectivesector 101. For the sake of example, each sector 101 is associated withat least one base station 103. In practice, one or more sectors 101 maynot include any base stations 103.

As shown, Global Optimization System (“GOS”) 105 may receive (at 102) RFmetrics associated with one or more sectors 101. The RF metrics mayinclude, for example, measures of signal quality, signal strength,interference, or the like, at given sectors 101 and/or locations withinsectors 101. Such measures may include and/or may be based on, forexample, Received Signal Strength Indicator (“RSSI”) values, ReferenceSignal Receive Power (“RSRP”) values,Signal-to-Interference-and-Noise-Ratio (“SINR”) values, Channel QualityIndicator (“CQI”) values, or the like.

In some embodiments, such measures may be included in, and/or derivedfrom, measurement reports received from UEs located within sectors 101.For example, a particular measurement report from a given UE mayindicate that the UE detected RF signals, according to a particular setof frequencies, from a first base station 103-1, and further that the UEdetected RF signals, according to the same set of frequencies, from asecond base station 103-2. In this scenario, the detection of RF signalsaccording to the same set of frequencies from two different base station(i.e., base station 103-1 and 103-2) may indicate RF interference at ageographical location of the UE, arising from signals output by basestations 103-1 and 103-2.

As another example, the measurement report from a given UE may indicatea relatively low RSSI value, RSRP value, etc. associated with signalsfrom a given base station 103. Such values may be “relatively” low inthat such values may be below a threshold value, and/or may be lowerthan an expected value (e.g., which may be determined based on ahistorical analysis of RF metrics). Further, such analysis may beperformed based on location, where a first threshold value may be usedat a first location (e.g., relatively close to base station 103), whilea second threshold value may be used at a second location (e.g., fartheraway from base station 103).

In some embodiments, and as further discussed below with respect to FIG.3 , GOS 105 may further receive and/or maintain attribute and/orcharacteristic information for one or more sectors 101. Briefly, suchattribute and/or characteristic information may include configurationparameters (e.g., beamforming configuration parameters, RF transmissionpower parameters, Multiple-Input Multiple-Output (“MIMO”) configurationparameters, or the like), physical network infrastructure parameters(e.g., antenna height, antenna location, etc.), locale features (e.g.,building density, topographical information, or the like), and/or othertypes of information associated with respective sectors 101 and/ornetwork infrastructure associated with respective sectors 101 (e.g.,network infrastructure located within given sectors 101, and/orproviding wireless service to given sectors 101).

In some embodiments, GOS 105 may communicate with base stations 103 ofsectors 101 and/or UEs located within such sectors 101 via anapplication programming interface (“API”), an X2 interface, and/or someother suitable communication pathway, in order to receive suchinformation. For example, base stations 103 and/or UEs communicativelycoupled to respective base stations 103 may “push” such information toGOS 105 (e.g., via the API) on a periodic or intermittent basis, uponthe occurrence of trigger events (e.g., the detection of a referencesignal from one or more base stations 103 by a UE located within a givensector 101, one or more Quality of Service (“QoS”) metrics exceeding athreshold value, a connection or disconnection of one or more UEs to oneor more base stations 103, and/or other events), and/or on some otherbasis. In some embodiments, GOS 105 may “pull” (e.g., request orotherwise obtain) such information from the UEs, base stations 103,and/or other device or system that receives, collects, maintains, and/orprovides such information. For example, GOS 105 may be communicativelycoupled to a Service Capability Exposure Function (“SCEF”) of a corenetwork associated with base stations 103, a Network Exposure Function(“NEF”), and/or other suitable device, system, function, etc.

As further shown, GOS 105 may determine (at 104) one or more sectormodels associated with respective sectors 101, as well as interferencemodels associated with respective sectors 101, based on the received RFmetrics. For example, as discussed below, GOS 105 may use AI/MLtechniques or other suitable techniques to identify one or more sectormodels that includes attributes that are similar to the attributesassociated with respective sectors 101. For example, when determiningwhether attributes of a given sector model are “similar” to attributesof a given sector 101, GOS 105 may generate one or more scores,classifiers, or the like, and/or may perform a suitable similarityanalysis to determine a measure of similarity between attributes of aset of sector models and attributes of a given sector 101. In someembodiments, GOS 105 may select a particular sector model if the measureof similarity exceeds a threshold measure of similarity. Additionally,or alternatively, GOS 105 may select a particular quantity ofhighest-scoring sector models (e.g., the highest scoring sector mode,the top three scoring sector models, etc.). In some embodiments, GOS 105may select a particular quantity of highest-scoring sector models, solong as the scores associated with such sector models exceeds athreshold score (e.g., the top three scoring sector models so long asthe top three scoring sector models exceed the threshold score, the toptwo scoring sector models if the third highest-scoring sector model isbelow the threshold score, etc.).

As further discussed in more detail below, GOS 105 may further determine(at 104) one or more interference models for one or more sectors 101based on the sector models identified with respect to respective sectors101, as well as the RF metrics received (at 102) with respect to therespective sectors 101. As one example, a particular interference modelmay indicate that reference signals from a particular base station 103of a particular sector 101 is causing interference with signals frombase stations 103 of the same sector 101 and/or of surrounding sectors101. As another example, a particular interference model may indicatethat an unknown interference source is present within a given sector101. In some embodiments, a particular interference model may indicatethat a transient interference source (e.g., a drone or some other mobileinterference source) is temporarily present within a given sector 101.

As additionally discussed below, GOS 105 may determine (at 104) one ormore remedial and/or performance-enhancing actions to perform withrespect to a given sector 101. For example, GOS 105 may determineactions such as modifying Physical Resource Blocks (“PRBs”) used by oneor more base stations 103 to carry reference signals (e.g.,modifications in the time and/or frequency domains of such signals),modifying a transmit power of reference signals (e.g., without modifyingthe transmit power of user plane signals, and/or modifying the transmitpower of user plane signals by a different amount than the modificationof the transmit power of reference signals). In some embodiments, theactions may include one or more other types of actions, such asmodifying a beamforming configuration of one or more base stations 103(e.g., beam width, power, azimuth angle, and/or tilt angle),implementing or modifying a cell suspend mode at one or more basestations 103, implementing or modifying a coordinated multi-pointconfiguration of multiple base stations 103 (e.g., base stations 103that interfere with each other), and/or other suitable actions to reduceinterference.

As noted above, the selection (at 104) of actions based not only on RFand/or interference-related metrics (e.g., based on interferencemodels), but also based on the characteristics and/or attributes of asector 101 (e.g., based on sector models), may allow for interferencesolutions that are better tailored to sectors with particularconfigurations, attributes, or the like. Such solutions may be morelikely to succeed and/or have more impact (e.g., reduction ofinterference) than actions selected solely based on the detection ofinterference in a given sector 101.

In some embodiments, GOS 105 may receive (at 102) RF metrics over time,and may select (at 104) different sets of actions (e.g., for particularsectors 101 and/or varying sets of sectors 101) based on different RFmetrics received at different times and/or time periods. As one example,a particular sector 101 may exhibit a first set of interference metricsat times corresponding to a morning or afternoon weekday commute, andmay exhibit a second set of interference metrics at times correspondingto an evening or weekend. In this example, GOS 105 may determine (at104) a first interference model (or set of interference models) andassociated actions during morning or afternoon hours on weekdays, andmay determine a second interference model (or set of interferencemodels) and one or more associated actions during evening hours and/orweekends.

GOS 105 may further output (at 106) information indicating theidentified actions to respective sectors 101. As discussed below, GOS105 may select particular sectors 101 (and/or network infrastructurelocated therein), out of the set of candidate sectors 101, to performthe actions based on one or more criteria (e.g., referred to as sector“dominance”). GOS 105 may, for example, indicate the determined actionsto respective base stations 103 associated with sectors 101, to amanagement device or system associated with one or more sectors 101,and/or some other device or system. For the sake of brevity, theperformance of a given action by a network infrastructure elementlocated in or serving sector 101 will be referred to herein as sector101 “performing” the action.

Respective sectors 101 may perform (at 108) the indicated actions, andGOS 105 may continue to receive (at 102) up-to-date RF and/orinterference metrics associated with sectors 101. GOS 105 may, based oncontinuing to receive the up-to-date RF and/or interference metrics,modify the determination of interference models associated with aparticular sector 101. In some embodiments, GOS 105 may select a new setof actions for sector 101 based on the up-to-date RF and/or interferencemetrics. In some embodiments, GOS 105 may modify one or more sectormodels, interference models, and/or other information based on whetherthe performed (at 108) actions reduced interference indicated by the RFand/or interference metrics, and/or based on how much effect the actionshad on such metrics.

While described in the context of being performed by GOS 105, in someembodiments, one or more devices or systems associated with sectors 101may perform one or more of the operations described above in lieu of, orin addition to, GOS 105. For example, in some embodiments, one or moredevices or systems of sector 101 may identify a particular action basedon a given sector model and/or interference model, and/or based oncontinuing to monitor RF and/or interference metrics associated withsector 101 after performing (at 108) a particular action or set ofactions.

FIG. 2 illustrates example interference models, sector models, and/oractions/parameters that may be generated, received, maintained,provided, etc. by GOS 105. For example, GOS 105 may be associated with aset of sector models 201, such as example sector models 201-1, 201-2,and 201-M. Further, GOS 105 may be associated with a set of interferencemodels 203, such as example interference models 203-1, 203-2, and 203-L.Additionally, GOS 105 may be associated with a set of actions/parameters205, such as example actions/parameters 205-1, 205-2, and 205-N.

GOS 105 may generate and/or modify sector models 201, interferencemodels 203, and/or actions/parameters 205 based on AI/ML techniques orother suitable techniques. For example, GOS 105 may generate, modify,refine, etc. sector models 201, interference models 203, and/oractions/parameters 205 based on an evaluation of real-world data fromsectors 101 and/or simulations of RF and/or interference metrics in asimulation and/or test environment. GOS 105 may further determine oridentify correlations between respective sector models 201, interferencemodels 203, and/or actions/parameters 205 using AI/ML techniques orother suitable techniques.

For example, as shown in FIG. 3 , sector model 201 may include RF and/orinterference metrics 301 (referred to simply as “RF metrics 301” for thesake of brevity), Quality of Service (“QoS”) metrics 303, energyconsumption metrics 305, RAN configuration parameters 307, inter-sectorinformation 309, locale features 311, and/or one or more other types ofinformation.

RF metrics 301 associated with a given sector 101 may include metricsrelated to the propagation of RF signals from network infrastructurewithin sector 101 (or providing service to sector 101). For example, RFmetrics 301 may include RSSI values, RSRP values, SINR values, CQIvalues, or other indicators of RF signal quality or strength. In someembodiments, RF metrics 301 may be determined by UEs or other RFsignal-receiving devices located within or near (e.g., within athreshold distance of) sector 101.

For example, a given UE may be configured to scan for the presence of RFsignals, such as reference signals, emitted by one or more base stations103. In some scenarios, such reference signals may be broadcasted (orotherwise transmitted) by base stations 103 at a higher transmissionpower than other transmissions output by base stations 103, such astransmissions carrying user plane data. The UE may, in some embodiments,generate one or more measurement reports, which may indicate a signalstrength of such transmissions received from one or more base stations103, and may further include an identifier of the base station(s) 103from which such transmissions were received.

The measurement reports may further include information regarding thetransmissions themselves, such as a frequency (or range of frequencies)on which the RF signals were detected by the UE, as well as timinginformation (e.g., timing offsets, frame, time slot, etc.) associatedwith the RF signals. In this manner, the PRBs associated with thereceived RF signals may be indicated or derived from the measurementreports, where a particular PRB refers to a particulartime-and-frequency slot in a time-and-frequency domain. A UE may also beconfigured to determine or report measures of interference, such asSINR, received RF signal power (e.g., at particular frequencies and/ortime slots), or other measures of interference.

In some embodiments, UEs may use device-to-device (“D2D”)communications, “direct” communications, personal area networks(“PANs”), or other suitable communication pathways to detect thepresence of other UEs. For example, the presence of multiple UEs withincommunication range of each other may be a factor that may contribute todetected interference. For example, the detection of interference by afirst UE coupled with a D2D detection of a second UE may suggest thatthe interference may be caused by, or contributed to by, the presence ofthe second UE.

As noted above, GOS 105 may receive the measurement reports and/or othersuitable RF metrics 301 from UEs (e.g., via an API or other suitablecommunication pathway), and/or from base stations 103 (e.g., via an X2interface or other suitable communication pathway, where base stations103 may receive measurement reports from UEs via Radio Resource Control(“RRC”) messaging or some other suitable communication pathway). In someembodiments, GOS 105 may receive RF metrics 301 from some other deviceor system.

Based on the received RF metrics 301, GOS 105 may determine a “baseline”or “expected” level of interference, received power, and/or other RFmetrics in sector 101. As discussed below, such “baseline” or “expected”levels may be determined on a granular basis (e.g., based on locationsor regions within given sectors 101, such as regions that are delineatedby distance and angle from a reference point). Further, the “baseline”or “expected” RF or interference levels may be determined on a temporalbasis, which may reflect fluctuations that vary on a periodic,repeating, or otherwise determinable basis (e.g., fluctuations based onweekday commutes, seasonal traffic, events at venues, or otherphenomena). In this manner, sector model 201 may be determined for agiven sector 101 based on temporal and/or spatial aspects of RF metrics301. Additionally, or alternatively, different sector models 201 may bedetermined for sector 101 (e.g., for different times or time periods,and/or for different locations or regions within sector 101).

QoS metrics 303 may reflect QoS metrics associated with a particularsector 101 over a particular period of time. For example, QoS metrics303 may include metrics relating to latency, bandwidth, jitter, packetloss, and/or other metrics related to network layer performance,application layer performance, or other “higher” layer performance(e.g., performance at a layer above a physical layer and/or a data linklayer). QoS metrics 303 associated with a given sector 101 may becollected from and/or reported by UEs receiving wireless service withinsector 101 and/or from a base station 103 located within sector 101,and/or may be received from base station 103 located in or providingwireless service to sector 101.

Energy consumption metrics 305 may indicate an amount of energy consumedat the particular sector 101 over the particular period of time. Forexample, energy consumption metrics 305 may indicate an amount ofelectrical power (e.g., kilowatt-hours or some other measure of consumedpower) consumed by network infrastructure elements (e.g., base stations103, data centers, routers, hubs, and/or other equipment) within orserving sector 101 over a given period of time.

RAN configuration parameters 307 may include parameters such as anindication of quantity and/or position (e.g., geographical position) ofphysical infrastructure hardware (e.g., antennas, radios, data centers,or the like) associated with one or more RANs in sector 101. In someembodiments, RAN configuration parameters 307 may indicate particularradio access technologies (“RATs”) implemented in sector 101 (e.g., aLTE RAT, a 5G RAT, etc.), beam configurations implemented in sector 101(e.g., beam quantity, beam azimuth angles, beam width, beam transmissionpower, etc.), antenna sensitivity (e.g., receive sensitivity), MIMOconfiguration information, and/or other suitable information. In someembodiments, RAN configuration parameters 307 may indicate the height ofone or more antennas associated with one or more base stations 103 (orother RF-emitting equipment), a coverage area of one or more antennas(e.g., a polygon, distance from a reference point, or other descriptorof geographical regions in which RF signals are received from the one ormore antennas), or other parameters of the one or more antennas.

In some embodiments, RAN configuration parameters 307 may includeinformation indicating a capacity or other capability of a given sector101 and/or one or more base stations 103 located in (or providingservice to) sector 101. For example, RAN configuration parameters 307may indicate an RF channel bandwidth within sector 101, an amount ofavailable and/or used PRBs associated with one or more base stations 103located in (or providing service to) sector 101, threshold quantities ofsupported UEs simultaneously connected to one or more base stations 103located in (or providing service to) sector 101, threshold amounts ofdata or throughput that may be sent and/or received by one or more basestations 103 located in (or providing service to) sector 101, and/orother capability and/or capacity-related information.

Inter-sector information 309 may include information associated withsectors adjacent to or proximate to a given sector 101. For example,inter-sector information 309 may include RF metrics, RAN parameters, QoSmetrics, and/or energy consumption metrics, associated with sectorsadjacent to or within a threshold distance of sector 101. In someembodiments, inter-sector information 309 may include mobilityinformation, which may be associated with mobility of UEs between sector101 and neighboring sectors. For example, inter-sector information 309may indicate that UEs that are located in sector 101 are likely to bestationary within sector 101 for a first duration of time (e.g.,approximately one hour), and then that such UEs travel to a particularneighboring sector. As another example, inter-sector information 309 mayindicate that UEs that are located in the neighboring sector arerelatively likely to enter the particular sector 101.

Locale features 311 may include information indicating attributes and/orfeatures of the geographical area. For example, locale features 311 mayinclude information relating to building layout and/or density,topographical features (e.g., mountains, valleys, forests, streams,etc.), weather-related information, air quality-related information(e.g., smog density, particulate density, fog density, etc.), and/orother factors that may affect RF metrics, energy consumption metrics,QoS metrics, or other metrics. Locale features 311 may includegeographical coordinates (e.g., latitude and longitude coordinates,Global Positioning System (“GPS”) coordinates, or the like) or othersuitable location information, to indicate the geographical locations ofrespective features.

As described below, a given sector 101 may be associated with one ormore sector models 201 based on a comparison of the above-describedfactors, and/or one or more other factors, of sector 101 to such factorsassociated with a set of candidate sector models 201. Briefly, forexample, GOS 105 may determine that a particular sector 101, thatexhibits a particular set of RF metrics 301, a particular set of QoSmetrics 303, a particular set of energy consumption metrics 305, and afirst set of locale features 311 (e.g., urban features such as high-risebuildings) is associated with a first sector model 201, while anothersector 101, that exhibits a similar set of RF metrics 301, a similar setof QoS metrics 303, and a similar set of energy consumption metrics 305,but a different second set of locale features 311 (e.g., rural featuressuch as relatively flat areas with relatively low building density) isassociated with a different second sector model 201.

Generally, a given sector model 201 may describe or reflect parameters,metrics, attributes, etc. of a given sector 101. As noted above, aparticular sector model 201 may include temporal or location-basedfluctuations or variations, and/or different sector models 201 may beassociated with a given sector 101 at different times or regions withinsector 101.

Interference models 203 may indicate different types or amounts ofinterference exhibited within a given sector 101. For example (asdescribed in more detail below with respect to FIGS. 4A-4C), aparticular interference model 203 may indicate that RF metricsassociated with a given sector 101, received over a particular timeperiod, do not match “baseline” or “expected” RF metrics (e.g., asindicated by one or more sector models 201 associated with sector 101).As another example (as described in more detail below with respect toFIG. 5 ), a particular interference model 203 may indicate thatcolliding RF signals (e.g., RF signals received from multiple antennas(e.g., other than situations in which signals from multiple antennas areexpected, such as a MIMO configuration, an antenna teamingconfiguration, or the like), base stations 103, or other types ofnetwork infrastructure hardware) have been detected within sector 101.

As shown in FIG. 4A, GOS 105 may determine (at 402) expectedinterference 401 associated with a particular sector 101. Expectedinterference 401 may be based on, for example, RF metrics 301 associatedwith sector 101 (e.g., historical RF metrics received over time). Insome embodiments, expected interference 401 may be determined based on aparticular sector model 201 associated with sector 101. As noted above,expected interference 401 may vary on a temporal basis (e.g., based ontime of day, day of week, season, etc.). Thus, the example describedhere is in the context of levels of expected interference 401 at aparticular time. Actual interference 403 may be received from sector 101(e.g., from one or more UEs within sector 101, from one or more basestations 103 located in or serving sector 101, and/or one or more othersuitable devices or systems). Generally, expected interference 401 mayrelate to historical RF metrics received, while actual interference 403may relate to RF metrics received in “real-time” or at “run time” (e.g.,GOS 105 may determine remedial actions to perform in order to reduceinterference indicated by actual interference 403 for sector 101).

In the examples of FIGS. 4A-4C, geographical regions associated with aparticular sector 101 are represented by shapes formed by arcs radiatingfrom a reference point as well as angles from the reference point. Thegeographical regions depicted in these figures may represent theentirety of sector 101, and/or may represent a portion of a particularsector. In some embodiments, the reference point may correspond to thelocation of one or more base stations 103, antennas, and/or otherRF-emitting network infrastructure hardware. In some embodiments, thereference point may correspond to a center point of sector 101, and/ormay be an arbitrary point within sector 101 (e.g., not necessarily basedon the location of a particular base station 103 or other equipment). Inpractice, interference may be determined on a per-sector basis, or onsome granular basis (e.g., different measures of interference trackedwithin sector 101) that is different from the examples shown here.

The shading in these figures generally represents amounts ofinterference detected at respective locations within sector 101. Forexample, darker shading may represent relatively more interference(and/or interference above a threshold level), while lighter shading (orno shading) may represent relatively less interference (and/orinterference below a threshold level). In some embodiments,“interference” may be measured or quantified as a difference between RFsignal power received at a receiver (e.g., a UE within sector 101) and areference RF signal power (e.g., a transmit power of one or more RFsignals of which the UE is in range). In some embodiments, interferencemay include, and/or may be based on, one or more RSRP values, SINRvalues, RSSI values, and/or other suitable values received from UEs orother devices within sector 101, and/or other suitable values ormeasures of interference.

As further shown, GOS 105 may determine (at 404) actual interference 403associated with sector 101. For example, GOS 105 may receive RF metrics,interference metrics, or the like from UEs and/or other devices orsystems located within sector 101. As similarly discussed above, actualinterference 403 may include, and/or may be based on, one or more RSRPvalues, SINR values, RSSI values, and/or other suitable values receivedfrom UEs or other devices within sector 101, and/or other suitablevalues or measures of interference.

As shown in FIG. 4B, GOS 105 may further determine (at 406) a differencebetween expected interference 401 and actual interference 403, in orderto determine excess interference 405. In some embodiments, GOS 105 mayperform a difference operation between expected interference 401 andactual interference 403 to determine the difference in interferencebetween expected interference 401 and actual interference 403. That is,interference indicated in actual interference 403, which is notindicated in expected interference 401, may be referred to as “excess”interference, as such actual interference 403 differs (e.g., is inexcess of) from levels of expected interference 401. For example, GOS105 may determine that actual interference 403 indicates that region407-1 is associated with a particular level of interference that is notpresent in expected interference 401. As such, region 407-1 may berepresented, in excess interference 405, as a shaded region. Further,since the corresponding level of interference in actual interference 403for region 407-1 is different from the relatively low (or no)interference in the same region as indicated in expected interference401, the level of interference (and the corresponding level of shadingthat represents such interference) in excess interference 405 may be thesame or about the same as shown in actual interference 403.

On the other hand, expected interference 401 and actual interference 403(e.g., where the numbering of regions 407 in excess interference 405refers to the same regions in expected interference 401 and actualinterference 403) may both reflect some level of interference at region407-2, where the interference for region 407-2 in actual interference403 is greater than the interference for region 407-2 in expectedinterference 401. As such, the shading in region 407-2, in excessinterference 405, may be a shade in between the shading shown inexpected interference 401 and actual interference 403 for region 407-2.

As a further example, expected interference 401 and actual interference403 may both indicate no interference (and/or a level of interferencebelow a threshold level of interference) at region 407-3. Accordingly,excess interference 405 may indicate no excess interference at region407-3 (e.g., no difference, or no difference above a thresholddifference in interference at region 407-3 in expected interference 401and actual interference 403). As yet another example, expectedinterference 401 and actual interference 403 may both indicate the samelevel of interference (and/or about the same level of interference) atregion 407-4. Accordingly, excess interference 405 may indicate noexcess interference at region 407-4 (e.g., no difference, or nodifference above a threshold difference in interference at region 407-4in expected interference 401 and actual interference 403).

As shown in FIG. 4C, GOS 105 may further select (at 410) a particularinterference model 203, from a set of candidate interference models 203,based on the identified excess interference 405, and further based onone or more sector models 201 associated with sector 101. For example,as described below, GOS 105 may use one or more AI/ML techniques todetermine an appropriate interference model 203 (e.g., interferencemodel 203-2, in this example) to represent the interference (e.g., theexcess interference) associated with sector 101, where the selection isfurther based on attributes of sector 101 (e.g., sector model 201).

As shown in FIG. 5 , GOS 105 may additionally, or alternatively,identify interference based on the detection of colliding signals at aparticular geographical location. For example, two example base stations103-1 and 103-2 may broadcast (at 502 and 504, respectively) referencesignals. As discussed above, base stations 103-1 and/or 103-2 maybroadcast the reference signals at a higher power than othertransmissions, such as transmissions related to RF signals carrying userplane data. Thus, while the RF signals carrying user plane data may notcollide, UE 501 may nevertheless detect (at 506) colliding referencesignals from base stations 103-1 and 103-2. For example, UE 501 maydetect that reference signals have been received on the same PRBs (e.g.,on the same frequencies and with the same timing) from base stations103-1 and 103-2. As another example, UE 501 may detect that referencesignals have been received on the same frequency or frequencies frombase stations 103-1 and 103-2 but with different timing, and/or thatreference signals have been received on a different frequency orfrequencies from base stations 103-1 and 103-2 but with the same timing.

Such a scenario may occur, for example, in a Dynamic Spectrum Sharing(“DSS”) configuration, in which base stations 103 may dynamicallyoperate at different frequency bands and/or according to different radioaccess technologies (“RATs”), such as a base station 103 that operatesaccording to a LTE RAT in certain scenarios (e.g., where a relativelylarge quantity or proportion of connected UEs have LTE capability butnot 5G capability), while operating according to a 5G RAT in otherscenarios (e.g., where a relatively large quantity or proportion ofconnected UEs have 5G capability and/or are sending and/or receivinglatency-sensitive traffic, such as traffic with a relatively high QoSlevel).

UE 501 may indicate (at 508) the detected colliding RF signals (e.g.,colliding reference signals) to GOS 105. For example, as discussedabove, UE 501 may provide such information to GOS 105 via an API, mayprovide such information to a connected base station 103 (e.g., basestation 103-1, base station 103-2, and/or some other base station 103 towhich UE 501 is connected) via one or more measurement reports, and/ormay otherwise provide the information to GOS 105 or some other devicesor systems that relays the information to GOS 105. The information fromUE 501 may include one or more cell identifiers or other suitableidentifiers of base station 103-1 and 103-2, from which the colliding RFsignals were detected. Further, the information from UE 501 may includegeographical location information associated with UE 501 at the time thecolliding RF signals were detected (at 506). In this manner, GOS 105 maybe capable of identifying the particular sources of interference (e.g.,the sources of the colliding RF signals), as well as a location at whichthe interference was detected.

GOS 105 may accordingly select (at 512) a particular interference model203 (e.g., interference model 203-2, in this example) based on theindicated colliding RF signal information, and further based on one ormore sector models 201 associated with the location of UE 501 (e.g., asector model 201 of a particular sector 101 in which UE 501 was locatedwhen detecting (at 506) the colliding RF signals).

As noted above, GOS 105 may generate, maintain, refine, etc. (e.g.,using one or more AI/ML techniques or other suitable techniques) one ormore associations between respective interference models 203 and one ormore sets of actions/parameters 205. In some embodiments, suchassociations may be multi-factor associations. For example, a first setof actions/parameters 205 may be associated with a particularinterference model 203 and a first sector model 201, while a second setof actions/parameters 205 may be associated with the same interferencemodel 203 and a second sector model 201. As another example, a first setof actions/parameters 205 may be associated with a first interferencemodel 203 and a particular sector model 201, while a second set ofactions/parameters 205 may be associated with a second interferencemodel 203 and the same sector model 201.

For example, each sector model 201-interference model 203 pair may beassociated with one or more sets of actions/parameters 205, as eachparticular set of actions/parameters 205 may have been determined (e.g.,based on real-world results and/or simulated results) as increasing theperformance (e.g., reducing interference) of one or more sectors 101that match a particular sector 101 that is associated with a particularsector model 201 and interference model 203. As noted above,actions/parameters 205 may include modifying reference signal PRBs(e.g., modifying the timing offsets and/or frequencies at whichreference signals are transmitted in a given sector 101), modifyingreference signal transmit power, activating or modifying a coordinatedmulti-point configuration, modifying beamforming parameters, and/orother suitable actions to reduce interference in sector 101 and/orsurrounding sectors 101.

In some embodiments, GOS 105 may also determine affinity scores and/orother correlations between sector models 201, interference models 203,and respective sets of actions/parameters 205. Such affinity scores maygenerally indicate how effective a given set of actions/parameters 205are for reducing interference in particular sector 101, given sectormodel 201 and interference model 203 associated with sector 101. Whenselecting a particular interference model 203 for sector 101 based onreceived or determined interference information (e.g., based on excessinterference 405, detected (at 506) colliding RF signals, and/or otherindications of interference), GOS 105 may select such interference model203 based on affinities, scores, correlations, or the like betweensector model 201 and interference model 203.

Similarly, when determining a particular set of actions/parameters 205for a particular sector 101, GOS 105 may select the particular set ofactions/parameters 205 from candidate sets of actions/parameters 205based on an affinity, score, correlation, etc. between sets ofactions/parameters 205 and sector model 201 and/or interference model203. Further, as discussed below, the particular actions/parameters 205may be determined based on a “dominance” of one or more sectors 101 atwhich interference has been detected, and/or one or more neighboringsectors 101 (e.g., adjacent sectors 101, and/or sectors 101 within athreshold distance of a particular sector 101).

FIG. 6 illustrates an example determination of one or more sector models201 for a particular sector 101. As shown, GOS 105 may determine (at602) parameters and/or attributes of sector 101. As discussed above,such parameters and/or attributes may include RF metrics 301, QoSmetrics 303, energy consumption metrics 305, RAN configurationparameters 307, inter-sector information 309, locale features 311,and/or other suitable parameters, attributes, metrics, or the like. GOS105 may further identify (at 604) one or more sector models 201 based onthe determined parameters and/or attributes of sector 101.

In this example, GOS 105 may determine that sector 101 is associatedwith a “highway” sector model 601-1 and a “media streaming” sector model601-3. As further shown, GOS 105 may not determine that sector 101 isassociated with an example “office building” sector model 601-2, or anexample “dense buildings” sector model 601-4. For example, GOS 105 maydetermine, based on a suitable similarity analysis of the parametersand/or attributes of sector 101, that sector models 601-2 and 601-4 donot match (e.g., correspond with a measure of similarity above athreshold measure of similarity) sector 101, and/or that sector models601-1 and 601-3 match (e.g., have a higher measure of similarity with)the parameters and/or attributes of sector 101 more closely. Asdiscussed above, operations 602 and 604 may be performed on an ongoingbasis, such that the selection of particular sector models 601 maychange based on updated parameters and/or attributes received by GOS 105over time.

FIGS. 7A-7C illustrate the determination of interference associated withone or more sectors 101, the selection of one or more sectors 101 toperform one or more remedial actions 205, and the performance of the oneor more actions 205 by the selected sector(s) 101. In FIG. 7A, forexample, GOS 105 may receive (at 702) RF metrics associated withmultiple sectors 101, such as sectors 101-1 and 101-2, as shown. Asdiscussed above, such RF metrics may be received from UEs 501 locatedwithin sectors 101-1 and/or 101-2, and/or from one or more devices orsystems that are located in and/or provide wireless service to sectors101-1 and/or 101-2. Further assume that GOS 105 has determinedappropriate sector models 201 that respectively correspond to sectors101-1 and 101-2 (e.g., as similarly described above).

GOS 105 may further determine (at 704) interference associated withsectors 101-1 and 101-2. For example, RF signals emitted by one or morebase stations 103 located in sector 101-1 may collide with RF signalsemitted by one or more base stations 103 located in sector 101-2. Assuch, UEs 501 located at the edges of sectors 101-1 and 101-2 (e.g.,near a border or intersection of sectors 101-1 and 101-2) may havedetected colliding signals (e.g., reference signals and/or other RFsignals) from base stations 103 located in sectors 101-1 and 101-2.

GOS 105 may determine (at 704) that sectors 101-1 and sectors 101-2 areassociated with one or more particular interference models 203 based onthe received RF metrics, as well as based on particular sector models201 associated with each one of sector 101-1 and 101-2, and/or based ona combination or union of sector models 201 associated with both sectors101-1 and 101-2 (and/or a particular sector model 201 associated withattributes of both sectors 101-1 and 101-2).

GOS 105 may, in some embodiments, select a particular sector 101 toperform remedial actions in situations where interference is detected atmultiple sectors 101 (e.g., adjacent sectors 101 and/or sectors 101within a threshold distance of each other). For example, as mentionedabove, GOS 105 may determine a “dominance” score associated with eachsector 101.

In some embodiments, the dominance score may be determined based onparticular attributes of sectors 101, and/or may be based on sectormodels 201 associated with sectors 101. For example, a sector 101 thatis associated with a relatively high elevation above sea level and/orwith one or more antennas that are located at a relatively high altitude(e.g., above a threshold level of altitude) may be associated with arelatively higher dominance score, while a sector 101 that is associatedwith a relatively low elevation above sea level and/or with one or moreantennas that are located at a relatively low altitude (e.g., below athreshold level of altitude) may be associated with a relatively lowerdominance score.

In some embodiments, network load may be a factor based on which adominance score for a particular sector 101 may be determined. Forexample, a sector 101 that is associated with a relatively high networkload (e.g., based on quantity of UEs 501 located within sector 101, aquantity of UEs 501 connected to base stations 103 located in sector101, an amount of traffic (e.g., throughput) sent or received by UEs 501located in sector 101, RF resource utilization (e.g., percentage ofavailable PRBs utilized for user plane data), and/or other measures ofnetwork load) may have a higher dominance score than a sector 101 withrelatively low network load.

In some embodiments, energy consumption may be a factor based on which adominance score for a particular sector 101 may be determined. Forexample, a sector 101 with greater level of energy consumption may havea higher dominance score than a sector 101 with a lower level of energyconsumption. In some embodiments, one or more other factors (e.g.,attributes of sectors 101, such as attributes of sector models 201associated with such sectors 101) may be used in determining a dominancescore for a given sector 101.

In some embodiments, active frequency bands and/or RATs may be a factorbased on which a dominance score for a particular sector 101 may bedetermined. For example, a sector 101 that is operating at a particularset of frequency bands or a particular RAT (e.g., a 5G RAT) may have ahigher dominance score than a sector 101 that is operating at adifferent set of frequency bands and/or a different RAT (e.g., a LTERAT).

In some embodiments, GOS 105 may select (at 704) sector 101-2 to performone or more actions to remediate the detected interference. For example,GOS 105 may have determined that a dominance score for sector 101-2 islower than a dominance score for sector 101-1. In some embodiments, asector with a higher dominance score may be selected to implement one ormore remedial actions.

As further shown in FIG. 7A, GOS 105 may instruct (at 706) sector 101-2to perform the selected remedial actions, and sector 101-2 may proceedto implement (at 708) instructed remedial actions. In some embodiments,some or all of operations 702-708 may repeat iteratively, such that theresult of the implemented actions may be evaluated by GOS 105 based oncontinued monitoring (e.g., at 702) of RF metrics after theimplementation (at 708) of the actions, and up-to-date interferencemodels and/or associated actions may be determined (at 704).

As another example, GOS 105 may determine that actual measuredinterference at sector 101-1 (based on RF metrics received at 702) isgreater than an expected level of interference (e.g., based onhistorical interference metrics and/or based on one or more sectormodels 201, as discussed above). GOS 105 may further determine that atransmit power associated with one or more base stations 103 of sector101-2 is higher than an expected transmit power, higher than aconfigured transmit power, and/or is otherwise higher than a previouslyused transmit power. In this situation, GOS 105 may determine that theinterference exhibited at sector 101-1 may have been caused by theincreased transmit power at sector 101-2. In this situation, GOS 105 maydetermine that the transmit power of one or more base stations 103 ofsector 101-1 should be reduced, so as not to collide with or otherwiseinterfere with RF signals from sector 101-2 (e.g., the RF signals fromsector 101-2 that are transmitted based on the increased transmit powerat sector 101-2). Alternatively, in some embodiments, GOS 105 maydetermine that the transmit power of one or more base stations 103 ofsector 101-2 should be reduced, as the increase in transmit power atsector 101-2 caused interference at sector 101-1. In some embodiments,GOS 105 may modify the transmission powers at both sectors 101, maycause a coordinated multi-point configuration of sectors 101-1 and 101-2to be implemented (e.g., where base stations 103 of both sectors 101-1and 101-2 coordinate on the frequency and/or timing of transmissions toavoid collisions), and/or may otherwise cause both sectors 101-1 and101-2 to perform actions to remediate the interference between sectors101-1 and 101-2.

For example, as shown in FIG. 7B, GOS 105 may receive (at 702) RFmetrics from sectors 101-1 and 101-2, determine (at 710) RF interferenceassociated with sectors sector 101-1 and 101-2, and may select bothsectors to perform remedial actions. GOS 105 may accordingly instruct(at 712) sectors 101-1 and 101-2 to perform the selected remedialactions, and sectors 101-1 and 101-2 may perform (at 714 and 716,respectively) the instructed remedial actions. For example, GOS 105 mayinstruct sectors 101-1 and 101-2 to modify (e.g., reduce) a transmitpower of reference signals and/or other RF signals, modify beamformingparameters of sectors 101-1 and/or 101-2 such that RF signals from thesesectors are not pointed at one another (and/or are less stronglydirected at one another), implement a coordinated multi-pointconfiguration, modify PRBs used for reference signals, and/or performother remedial actions. In some embodiments, sectors 101-1 and 101-2 maybe instructed to perform the same remedial actions or different remedialactions.

As another example, as shown in FIG. 7C, GOS 105 may receive (at 702) RFmetrics from sectors 101-1, 101-2, and 101-3, and determine (at 718) RFinterference associated with sectors sector 101-1, 101-2, and 101-3. Inthis example, further assume that GOS 105 determines that sector 101-3has a highest dominance score out of sectors 101-1, 101-2, and 101-3. Inaccordance with some embodiments, GOS 105 may select (at 718) one ormore remedial actions for sector 101-1 and 101-2 to perform, whileforgoing selecting actions for sector 101-3 to perform, based on sector101-3 having the highest dominance score of these sectors. GOS 105 mayaccordingly instruct (at 720) sectors 101-1 and 101-2 to perform theselected remedial actions, and sectors 101-1 and 101-2 may perform (at722 and 724, respectively) the instructed remedial actions.

FIG. 8 illustrates an example process 800 for determining one or moresector models, interference models, and/or sets of actions to performwith respect to a given sector associated with a RAN of a wirelessnetwork. In some embodiments, some or all of process 800 may beperformed by GOS 105. In some embodiments, one or more other devices mayperform some or all of process 800 in concert with, and/or in lieu of,GOS 105, such as one or more devices or systems associated with one ormore sectors 101.

As shown, process 800 may include generating, receiving, and/ormodifying (at 802) one or more sector models 201 based on metrics,parameters, etc. associated with one or more sectors 101 of a wirelessnetwork. For example, as discussed above, GOS 105 may use AI/MLtechniques or other suitable techniques to generate and/or refine sectormodels 201. For example, GOS 105 may evaluate metrics based on real-wordand/or simulated KPIs and/or attributes of one or more sectors 101 inorder to generate one or more clusters, classifications, or the like,which may be reflected by sector models 201.

Process 800 may further include generating, receiving, and/or modifying(at 804) one or more interference models associated with particularamounts and/or types of RF interference. For example, as discussedabove, GOS 105 may evaluate measures of RF interference (e.g., includingor based on RSSI values, SINR values, RSRP values, and/or other suitablemeasures of interference), types of RF interference (e.g., the detectionof colliding RF signals from multiple sources such as multiple basestations 103, the detection of reference signals from multiple sources,the detection of a difference in actual interference as compared toexpected interference, and/or other types of RF interference). Suchchannel RF interference metrics may be received from UEs 501 connectedto one or more base stations 103 (e.g., real-world measured or computedmetrics), from one or more base stations 103 and/or other wirelessnetwork infrastructure elements, and/or may be generated or receivedbased on a simulation of a RAN (e.g., in which channel conditionsbetween one or more UEs 501 and one or more base stations 103 aresimulated).

Process 800 may additionally include determining (at 806) a particularsector model associated with a particular sector 101 of a RAN based onattributes of the particular sector 101. For example, as discussedabove, GOS 105 may receive information associated with sector 101, suchas RF metrics 301, QoS metrics 303, energy consumption metrics 305, RANconfiguration parameters 307, inter-sector information 309, localefeatures 311, and/or other suitable information, which GOS 105 maycompare to attributes of one or more sector models 201, in order todetermine one or more particular sector models 201 associated withsector 101. For example, as discussed above, GOS 105 may use one or moreAI/ML techniques to determine an association, correlation, or the likebetween the attributes associated with sector 101 and one or more sectormodels 201. For example, GOS 105 may select a particular sector model201 from a set of candidate sector models 201, and/or may generate a newsector model 201 based on the attributes of sector 101.

Process 800 may also include receiving (at 808) RF metrics associatedwith the particular sector 101. For example, GOS 105 may monitor,receive, etc. reference metrics associated with the particular sector101. In some embodiments, as discussed above, the RF metrics mayinclude, and/or may be based on, measurement reports generated by one ormore UEs 501 located in and/or connected to one or more base stations103 associated with sector 101. In some embodiments, the RF metrics maybe generated by one or more base stations 103 and/or other devices orsystems located in or serving sector 101.

Process 800 may further include identifying (at 810) RF interferencewithin sector 101 based on the received RF metrics. For example, GOS 105may determine, based on the RF metrics, that measures of RF interferenceexceed threshold levels, that measures of RF interference differ fromhistorical RF interference levels, that colliding RF signals (e.g., frommultiple sources) have been detected within sector 101, and/or thatother types of RF interference have been detected.

Process 800 may additionally include selecting (at 812) a particularinterference model 203 based on the identified RF interference. Forexample, GOS 105 may use a suitable correlation, similarity, etc.analysis to identify one or more interference models 203, out of a setof candidate interference models 203, that match (e.g., within athreshold level of similarity) the type and/or amount of identified RFinterference within sector 101.

Process 800 may also include determining (at 814) a set of actions basedon the selected interference model 203 and sector model 201. Forexample, as discussed above, GOS 105 may use AI/ML techniques or othersuitable techniques to identify a particular set of actions associatedwith the selected interference model 203 and sector model 201. Forexample, as discussed above, GOS 105 may maintain or determine one ormore affinity scores or other suitable scores between sets of actions205, interference models 203, and/or sector models 201, based on which aparticular set of actions 205 may be selected.

Process 800 may further include implementing (at 816) the identified setof actions. For example, as discussed above, sector 101 may make one ormore adjustments to parameters (e.g., frequency bands and/or timingoffsets used for particular RF signals such as reference signals),physical devices (e.g., antennas), or the like based on the identifiedset of actions/parameters 205.

As shown in FIG. 8 , some or all of process 800 may be performed and/orrepeated iteratively. For example, some or all of operations 808-816 maybe repeated and/or performed, in order to continuously remediatepotential RF interference within a given sector 101. That is, theresults of implementing (at 816) particular actions in response toparticular interference models 203 associated with particular sectormodels 201 may be evaluated (e.g., based on continued monitoring (at808)). Further, the associations or affinity scores between sectormodels 201, interference models 203, and sets of actions/parameters 205may be modified (e.g., strengthened or weakened) based on whetherparticular actions 205 improved interference within sector 101 and/orsurrounding sectors 101. For example, if a particular action 205 reducedRF interference (e.g., reduced compared to a previous level of RFinterference, and/or reduced RF interference below a threshold level),an affinity score between particular action 205 and an appropriatesector model 201 and/or interference model 203 for sector 101 may beincreased. If, on the other hand, a particular action 205 increasedand/or did not affect RF interference (e.g., did not reduce RFinterference compared to a previous level of RF interference, and/or didnot reduce RF interference below a threshold level), an affinity scorebetween particular action 205 and an appropriate sector model 201 and/orinterference model 203 for sector 101 may be decreased, thus reducing oreliminating the likelihood that the same action 205 is selected infuture instances of similar types of RF interference detected at sectorshaving similar attributes as particular sector 101.

FIG. 9 illustrates an example environment 900, in which one or moreembodiments may be implemented. In some embodiments, environment 900 maycorrespond to a 5G network, and/or may include elements of a 5G network.In some embodiments, environment 900 may correspond to a 5GNon-Standalone (“NSA”) architecture, in which a 5G RAT may be used inconjunction with one or more other RATs (e.g., a LTE RAT), and/or inwhich elements of a 5G core network may be implemented by, may becommunicatively coupled with, and/or may include elements of anothertype of core network (e.g., an evolved packet core (“EPC”)). As shown,environment 900 may include UE 501, RAN 910 (which may include one ormore Next Generation Node Bs (“gNBs”) 911), RAN 912 (which may includeone or more one or more evolved Node Bs (“eNBs”) 913), and variousnetwork functions such as Access and Mobility Management Function(“AMF”) 915, Mobility Management Entity (“MME”) 916, Serving Gateway(“SGW”) 917, Session Management Function (“SMF”)/Packet Data Network(“PDN”) Gateway (“PGW”)-Control plane function (“PGW-C”) 920, PolicyControl Function (“PCF”)/Policy Charging and Rules Function (“PCRF”)925, Application Function (“AF”) 930, User Plane Function(“UPF”)/PGW-User plane function (“PGW-U”) 935, Home Subscriber Server(“HSS”)/Unified Data Management (“UDM”) 940, and Authentication ServerFunction (“AUSF”) 945. Environment 900 may also include one or morenetworks, such as Data Network (“DN”) 950. Environment 900 may includeone or more additional devices or systems communicatively coupled to oneor more networks (e.g., DN 950), such as GOS 105.

The example shown in FIG. 9 illustrates one instance of each networkcomponent or function (e.g., one instance of SMF/PGW-C 920, PCF/PCRF925, UPF/PGW-U 935, HSS/UDM 940, and/or 945). In practice, environment900 may include multiple instances of such components or functions. Forexample, in some embodiments, environment 900 may include multiple“slices” of a core network, where each slice includes a discrete set ofnetwork functions (e.g., one slice may include a first instance ofSMF/PGW-C 920, PCF/PCRF 925, UPF/PGW-U 935, HSS/UDM 940, and/or 945,while another slice may include a second instance of SMF/PGW-C 920,PCF/PCRF 925, UPF/PGW-U 935, HSS/UDM 940, and/or 945). The differentslices may provide differentiated levels of service, such as service inaccordance with different QoS parameters.

The quantity of devices and/or networks, illustrated in FIG. 9 , isprovided for explanatory purposes only. In practice, environment 900 mayinclude additional devices and/or networks, fewer devices and/ornetworks, different devices and/or networks, or differently arrangeddevices and/or networks than illustrated in FIG. 9 . For example, whilenot shown, environment 900 may include devices that facilitate or enablecommunication between various components shown in environment 900, suchas routers, modems, gateways, switches, hubs, etc. Alternatively, oradditionally, one or more of the devices of environment 900 may performone or more network functions described as being performed by anotherone or more of the devices of environment 900. Devices of environment900 may interconnect with each other and/or other devices via wiredconnections, wireless connections, or a combination of wired andwireless connections. In some implementations, one or more devices ofenvironment 900 may be physically integrated in, and/or may bephysically attached to, one or more other devices of environment 900.

UE 501 may include a computation and communication device, such as awireless mobile communication device that is capable of communicatingwith RAN 910, RAN 912, and/or DN 950. UE 501 may be, or may include, aradiotelephone, a personal communications system (“PCS”) terminal (e.g.,a device that combines a cellular radiotelephone with data processingand data communications capabilities), a personal digital assistant(“PDA”) (e.g., a device that may include a radiotelephone, a pager,Internet/intranet access, etc.), a smart phone, a laptop computer, atablet computer, a camera, a personal gaming system, an IoT device(e.g., a sensor, a smart home appliance, or the like), a wearabledevice, an Internet of Things (“IoT”) device, a Mobile-to-Mobile (“M2M”)device, or another type of mobile computation and communication device.UE 501 may send traffic to and/or receive traffic (e.g., user planetraffic) from DN 950 via RAN 910, RAN 912, and/or UPF/PGW-U 935.

RAN 910 may be, or may include, a 5G RAN that includes one or more basestations (e.g., one or more gNBs 911), via which UE 501 may communicatewith one or more other elements of environment 900. UE 501 maycommunicate with RAN 910 via an air interface (e.g., as provided by gNB911). For instance, RAN 910 may receive traffic (e.g., voice calltraffic, data traffic, messaging traffic, signaling traffic, etc.) fromUE 501 via the air interface, and may communicate the traffic toUPF/PGW-U 935, and/or one or more other devices or networks. Similarly,RAN 910 may receive traffic intended for UE 501 (e.g., from UPF/PGW-U935, AMF 915, and/or one or more other devices or networks) and maycommunicate the traffic to UE 501 via the air interface. In someembodiments, base station 103 may be, may include, and/or may beimplemented by one or more gNBs 911.

RAN 912 may be, or may include, a LTE RAN that includes one or more basestations (e.g., one or more eNBs 913), via which UE 501 may communicatewith one or more other elements of environment 900. UE 501 maycommunicate with RAN 912 via an air interface (e.g., as provided by eNB913). For instance, RAN 910 may receive traffic (e.g., voice calltraffic, data traffic, messaging traffic, signaling traffic, etc.) fromUE 501 via the air interface, and may communicate the traffic toUPF/PGW-U 935, and/or one or more other devices or networks. Similarly,RAN 910 may receive traffic intended for UE 501 (e.g., from UPF/PGW-U935, SGW 917, and/or one or more other devices or networks) and maycommunicate the traffic to UE 501 via the air interface. In someembodiments, base station 103 may be, may include, and/or may beimplemented by one or more eNBs 913.

AMF 915 may include one or more devices, systems, Virtualized NetworkFunctions (“VNFs”), etc., that perform operations to register UE 501with the 5G network, to establish bearer channels associated with asession with UE 501, to hand off UE 501 from the 5G network to anothernetwork, to hand off UE 501 from the other network to the 5G network,manage mobility of UE 501 between RANs 910 and/or gNBs 911, and/or toperform other operations. In some embodiments, the 5G network mayinclude multiple AMFs 915, which communicate with each other via the N14interface (denoted in FIG. 9 by the line marked “N14” originating andterminating at AMF 915).

MME 916 may include one or more devices, systems, VNFs, etc., thatperform operations to register UE 501 with the EPC, to establish bearerchannels associated with a session with UE 501, to hand off UE 501 fromthe EPC to another network, to hand off UE 501 from another network tothe EPC, manage mobility of UE 501 between RANs 912 and/or eNBs 913,and/or to perform other operations.

SGW 917 may include one or more devices, systems, VNFs, etc., thataggregate traffic received from one or more eNBs 913 and send theaggregated traffic to an external network or device via UPF/PGW-U 935.Additionally, SGW 917 may aggregate traffic received from one or moreUPF/PGW-Us 935 and may send the aggregated traffic to one or more eNBs913. SGW 917 may operate as an anchor for the user plane duringinter-eNB handovers and as an anchor for mobility between differenttelecommunication networks or RANs (e.g., RANs 910 and 912).

SMF/PGW-C 920 may include one or more devices, systems, VNFs, etc., thatgather, process, store, and/or provide information in a manner describedherein. SMF/PGW-C 920 may, for example, facilitate in the establishmentof communication sessions on behalf of UE 501. In some embodiments, theestablishment of communications sessions may be performed in accordancewith one or more policies provided by PCF/PCRF 925.

PCF/PCRF 925 may include one or more devices, systems, VNFs, etc., thataggregate information to and from the 5G network and/or other sources.PCF/PCRF 925 may receive information regarding policies and/orsubscriptions from one or more sources, such as subscriber databasesand/or from one or more users (such as, for example, an administratorassociated with PCF/PCRF 925).

AF 930 may include one or more devices, systems, VNFs, etc., thatreceive, store, and/or provide information that may be used indetermining parameters (e.g., quality of service parameters, chargingparameters, or the like) for certain applications.

UPF/PGW-U 935 may include one or more devices, systems, VNFs, etc., thatreceive, store, and/or provide data (e.g., user plane data). Forexample, UPF/PGW-U 935 may receive user plane data (e.g., voice calltraffic, data traffic, etc.), destined for UE 501, from DN 950, and mayforward the user plane data toward UE 501 (e.g., via RAN 910, SMF/PGW-C920, and/or one or more other devices). In some embodiments, multipleUPFs 935 may be deployed (e.g., in different geographical locations),and the delivery of content to UE 501 may be coordinated via the N9interface (e.g., as denoted in FIG. 9 by the line marked “N9”originating and terminating at UPF/PGW-U 935). Similarly, UPF/PGW-U 935may receive traffic from UE 501 (e.g., via RAN 910, SMF/PGW-C 920,and/or one or more other devices), and may forward the traffic toward DN950. In some embodiments, UPF/PGW-U 935 may communicate (e.g., via theN4 interface) with SMF/PGW-C 920, regarding user plane data processed byUPF/PGW-U 935.

HSS/UDM 940 and AUSF 945 may include one or more devices, systems, VNFs,etc., that manage, update, and/or store, in one or more memory devicesassociated with AUSF 945 and/or HSS/UDM 940, profile informationassociated with a subscriber. AUSF 945 and/or HSS/UDM 940 may performauthentication, authorization, and/or accounting operations associatedwith the subscriber and/or a communication session with UE 501.

DN 950 may include one or more wired and/or wireless networks. Forexample, DN 950 may include an Internet Protocol (“IP”)-based PDN, awide area network (“WAN”) such as the Internet, a private enterprisenetwork, and/or one or more other networks. UE 501 may communicate,through DN 950, with data servers, other UEs 501, and/or to otherservers or applications that are coupled to DN 950. DN 950 may beconnected to one or more other networks, such as a public switchedtelephone network (“PSTN”), a public land mobile network (“PLMN”),and/or another network. DN 950 may be connected to one or more devices,such as content providers, applications, web servers, and/or otherdevices, with which UE 501 may communicate.

GOS 105 may include one or more devices, systems, VNFs, etc. thatperform one or more operations described above. For example, GOS 105 maygenerate and/or maintain sector models 201, interference models 203,and/or sets of actions and/or parameters 205. Further GOS 105 maydetermine associations between respective sector models 201,interference models 203, and/or sets of actions and/or parameters 205.GOS 105 may identify particular sectors 101 to be remediated, improved,etc., and may identify sector models 201, interference models 203,and/or actions 205 to perform with respect to such sectors 101, asdescribed above. GOS 105 may communicate with gNBs 911 and/or eNBs 913via an X2 interface, may receive UE information and/or other networkinformation from HSS/UDM 940 via a suitable API or other communicationpathway, and/or may communicate with UEs 501 via gNBs 911 and/or eNBs913.

FIG. 10 illustrates an example Distributed Unit (“DU”) network 1000,which may be included in and/or implemented by one or more RANs (e.g.,RAN 910, RAN 912, or some other RAN). In some embodiments, a particularRAN may include one DU network 1000. In some embodiments, a particularRAN may include multiple DU networks 1000. In some embodiments, DUnetwork 1000 may correspond to a particular gNB 911 of a 5G RAN (e.g.,RAN 910). In some embodiments, DU network 1000 may correspond tomultiple gNBs 911. In some embodiments, DU network 1000 may correspondto one or more other types of base stations of one or more other typesof RANs. As shown, DU network 1000 may include Central Unit (“CU”) 1005,one or more Distributed Units (“DUs”) 1003-1 through 1003-N (referred toindividually as “DU 1003,” or collectively as “DUs 1003”), and one ormore Radio Units (“RUs”) 1001-1 through 1001-M (referred to individuallyas “RU 1001,” or collectively as “RUs 1001”).

CU 1005 may communicate with a core of a wireless network (e.g., maycommunicate with one or more of the devices or systems described abovewith respect to FIG. 9 , such as AMF 915 and/or UPF/PGW-U 935). In theuplink direction (e.g., for traffic from UEs 501 to a core network), CU1005 may aggregate traffic from DUs 1003, and forward the aggregatedtraffic to the core network. In some embodiments, CU 1005 may receivetraffic according to a given protocol (e.g., Radio Link Control (“RLC”))from DUs 1003, and may perform higher-layer processing (e.g., mayaggregate/process RLC packets and generate Packet Data ConvergenceProtocol (“PDCP”) packets based on the RLC packets) on the trafficreceived from DUs 1003.

In accordance with some embodiments, CU 1005 may receive downlinktraffic (e.g., traffic from the core network) for a particular UE 501,and may determine which DU(s) 1003 should receive the downlink traffic.DU 1003 may include one or more devices that transmit traffic between acore network (e.g., via CU 1005) and UE 501 (e.g., via a respective RU1001). DU 1003 may, for example, receive traffic from RU 1001 at a firstlayer (e.g., physical (“PHY”) layer traffic, or lower PHY layertraffic), and may process/aggregate the traffic to a second layer (e.g.,upper PHY and/or RLC). DU 1003 may receive traffic from CU 1005 at thesecond layer, may process the traffic to the first layer, and providethe processed traffic to a respective RU 1001 for transmission to UE501.

RU 1001 may include hardware circuitry (e.g., one or more RFtransceivers, antennas, radios, and/or other suitable hardware) tocommunicate wirelessly (e.g., via an RF interface) with one or more UEs501, one or more other DUs 1003 (e.g., via RUs 1001 associated with DUs1003), and/or any other suitable type of device. In the uplinkdirection, RU 1001 may receive traffic from UE 501 and/or another DU1003 via the RF interface and may provide the traffic to DU 1003. In thedownlink direction, RU 1001 may receive traffic from DU 1003, and mayprovide the traffic to UE 501 and/or another DU 1003.

RUs 1001 may, in some embodiments, be communicatively coupled to one ormore Multi-Access/Mobile Edge Computing (“MEC”) devices, referred tosometimes herein simply as (“MECs”) 1007. For example, RU 1001-1 may becommunicatively coupled to MEC 1007-1, RU 1001-M may be communicativelycoupled to MEC 1007-M, DU 1003-1 may be communicatively coupled to MEC1007-2, DU 1003-N may be communicatively coupled to MEC 1007-N, CU 1005may be communicatively coupled to MEC 1007-3, and so on. MECs 1007 mayinclude hardware resources (e.g., configurable or provisionable hardwareresources) that may be configured to provide services and/or otherwiseprocess traffic to and/or from UE 501, via a respective RU 1001.

For example, RU 1001-1 may route some traffic, from UE 501, to MEC1007-1 instead of to a core network (e.g., via DU 1003 and CU 1005). MEC1007-1 may process the traffic, perform one or more computations basedon the received traffic, and may provide traffic to UE 501 via RU1001-1. In this manner, ultra-low latency services may be provided to UE501, as traffic does not need to traverse DU 1003, CU 1005, and anintervening backhaul network between DU network 1000 and the corenetwork. In some embodiments, MEC 1007 may include, and/or mayimplement, some or all of the functionality described above with respectto GOS 105.

FIG. 11 illustrates an example O-RAN environment 1100, which maycorrespond to RAN 910, RAN 912, and/or DU network 1000. For example, RAN910, RAN 912, and/or DU network 1000 may include one or more instancesof O-RAN environment 1100, and/or one or more instances of O-RANenvironment 1100 may implement RAN 910, RAN 912, DU network 1000, and/orsome portion thereof. As shown, O-RAN environment 1100 may includeNon-Real Time Radio Intelligent Controller (“RIC”) 1101, Near-Real TimeRIC 1103, O-eNB 1105, O-CU-Control Plane (“O-CU-CP”) 1107, O-CU-UserPlane (“O-CU-UP”) 1109, O-DU 1111, O-RU 1113, and O-Cloud 1115. In someembodiments, O-RAN environment 1100 may include additional, fewer,different, and/or differently arranged components.

In some embodiments, some or all of the elements of O-RAN environment1100 may be implemented by one or more configurable or provisionableresources, such as virtual machines, cloud computing systems, physicalservers, and/or other types of configurable or provisionable resources.In some embodiments, some or all of O-RAN environment 1100 may beimplemented by, and/or communicatively coupled to, one or more MECs1007.

Non-Real Time RIC 1101 and Near-Real Time RIC 1103 may receiveperformance information (and/or other types of information) from one ormore sources, and may configure other elements of O-RAN environment 1100based on such performance or other information. For example, Near-RealTime RIC 1103 may receive performance information, via one or more E2interfaces, from O-eNB 1105, O-CU-CP 1107, and/or O-CU-UP 1109, and maymodify parameters associated with O-eNB 1105, O-CU-CP 1107, and/orO-CU-UP 1109 based on such performance information. Similarly, Non-RealTime RIC 1101 may receive performance information associated with O-eNB1105, O-CU-CP 1107, O-CU-UP 1109, and/or one or more other elements ofO-RAN environment 1100 and may utilize machine learning and/or otherhigher level computing or processing to determine modifications to theconfiguration of O-eNB 1105, O-CU-CP 1107, O-CU-UP 1109, and/or otherelements of O-RAN environment 1100. In some embodiments, Non-Real TimeRIC 1101 may generate machine learning models based on performanceinformation associated with O-RAN environment 1100 or other sources, andmay provide such models to Near-Real Time RIC 1103 for implementation.

O-eNB 1105 may perform functions similar to those described above withrespect to eNB 913. For example, O-eNB 1105 may facilitate wirelesscommunications between UE 501 and a core network. O-CU-CP 1107 mayperform control plane signaling to coordinate the aggregation and/ordistribution of traffic via one or more DUs 1003, which may includeand/or be implemented by one or more O-DUs 1111, and O-CU-UP 1109 mayperform the aggregation and/or distribution of traffic via such DUs 1003(e.g., O-DUs 1111). O-DU 1111 may be communicatively coupled to one ormore RUs 1001, which may include and/or may be implemented by one ormore O-RUs 1113. In some embodiments, O-Cloud 1115 may include or beimplemented by one or more MECs 1007, which may provide services, andmay be communicatively coupled, to O-CU-CP 1107, O-CU-UP 1109, O-DU1111, and/or O-RU 1113 (e.g., via an O1 and/or O2 interface).

FIG. 12 illustrates example components of device 1200. One or more ofthe devices described above may include one or more devices 1200. Device1200 may include bus 1210, processor 1220, memory 1230, input component1240, output component 1250, and communication interface 1260. Inanother implementation, device 1200 may include additional, fewer,different, or differently arranged components.

Bus 1210 may include one or more communication paths that permitcommunication among the components of device 1200. Processor 1220 mayinclude a processor, microprocessor, or processing logic that mayinterpret and execute instructions. Memory 1230 may include any type ofdynamic storage device that may store information and instructions forexecution by processor 1220, and/or any type of non-volatile storagedevice that may store information for use by processor 1220.

Input component 1240 may include a mechanism that permits an operator toinput information to device 1200 and/or other receives or detects inputfrom a source external to 1240, such as a touchpad, a touchscreen, akeyboard, a keypad, a button, a switch, a microphone or other audioinput component, etc. In some embodiments, input component 1240 mayinclude, or may be communicatively coupled to, one or more sensors, suchas a motion sensor (e.g., which may be or may include a gyroscope,accelerometer, or the like), a location sensor (e.g., a GlobalPositioning System (“GPS”)-based location sensor or some other suitabletype of location sensor or location determination component), athermometer, a barometer, and/or some other type of sensor. Outputcomponent 1250 may include a mechanism that outputs information to theoperator, such as a display, a speaker, one or more light emittingdiodes (“LEDs”), etc.

Communication interface 1260 may include any transceiver-like mechanismthat enables device 1200 to communicate with other devices and/orsystems. For example, communication interface 1260 may include anEthernet interface, an optical interface, a coaxial interface, or thelike. Communication interface 1260 may include a wireless communicationdevice, such as an infrared (“IR”) receiver, a Bluetooth® radio, or thelike. The wireless communication device may be coupled to an externaldevice, such as a remote control, a wireless keyboard, a mobiletelephone, etc. In some embodiments, device 1200 may include more thanone communication interface 1260. For instance, device 1200 may includean optical interface and an Ethernet interface.

Device 1200 may perform certain operations relating to one or moreprocesses described above. Device 1200 may perform these operations inresponse to processor 1220 executing software instructions stored in acomputer-readable medium, such as memory 1230. A computer-readablemedium may be defined as a non-transitory memory device. A memory devicemay include space within a single physical memory device or spreadacross multiple physical memory devices. The software instructions maybe read into memory 1230 from another computer-readable medium or fromanother device. The software instructions stored in memory 1230 maycause processor 1220 to perform processes described herein.Alternatively, hardwired circuitry may be used in place of or incombination with software instructions to implement processes describedherein. Thus, implementations described herein are not limited to anyspecific combination of hardware circuitry and software.

The foregoing description of implementations provides illustration anddescription, but is not intended to be exhaustive or to limit thepossible implementations to the precise form disclosed. Modificationsand variations are possible in light of the above disclosure or may beacquired from practice of the implementations.

For example, while series of blocks and/or signals have been describedabove (e.g., with regard to FIGS. 1-8 ), the order of the blocks and/orsignals may be modified in other implementations. Further, non-dependentblocks and/or signals may be performed in parallel. Additionally, whilethe figures have been described in the context of particular devicesperforming particular acts, in practice, one or more other devices mayperform some or all of these acts in lieu of, or in addition to, theabove-mentioned devices.

The actual software code or specialized control hardware used toimplement an embodiment is not limiting of the embodiment. Thus, theoperation and behavior of the embodiment has been described withoutreference to the specific software code, it being understood thatsoftware and control hardware may be designed based on the descriptionherein.

In the preceding specification, various example embodiments have beendescribed with reference to the accompanying drawings. It will, however,be evident that various modifications and changes may be made thereto,and additional embodiments may be implemented, without departing fromthe broader scope of the invention as set forth in the claims thatfollow. The specification and drawings are accordingly to be regarded inan illustrative rather than restrictive sense.

Even though particular combinations of features are recited in theclaims and/or disclosed in the specification, these combinations are notintended to limit the disclosure of the possible implementations. Infact, many of these features may be combined in ways not specificallyrecited in the claims and/or disclosed in the specification. Althougheach dependent claim listed below may directly depend on only one otherclaim, the disclosure of the possible implementations includes eachdependent claim in combination with every other claim in the claim set.

Further, while certain connections or devices are shown, in practice,additional, fewer, or different, connections or devices may be used.Furthermore, while various devices and networks are shown separately, inpractice, the functionality of multiple devices may be performed by asingle device, or the functionality of one device may be performed bymultiple devices. Further, multiple ones of the illustrated networks maybe included in a single network, or a particular network may includemultiple networks. Further, while some devices are shown ascommunicating with a network, some such devices may be incorporated, inwhole or in part, as a part of the network.

To the extent the aforementioned implementations collect, store, oremploy personal information of individuals, groups or other entities, itshould be understood that such information shall be used in accordancewith all applicable laws concerning protection of personal information.Additionally, the collection, storage, and use of such information canbe subject to consent of the individual to such activity, for example,through well known “opt-in” or “opt-out” processes as can be appropriatefor the situation and type of information. Storage and use of personalinformation can be in an appropriately secure manner reflective of thetype of information, for example, through various access control,encryption and anonymization techniques for particularly sensitiveinformation.

No element, act, or instruction used in the present application shouldbe construed as critical or essential unless explicitly described assuch. An instance of the use of the term “and,” as used herein, does notnecessarily preclude the interpretation that the phrase “and/or” wasintended in that instance. Similarly, an instance of the use of the term“or,” as used herein, does not necessarily preclude the interpretationthat the phrase “and/or” was intended in that instance. Also, as usedherein, the article “a” is intended to include one or more items, andmay be used interchangeably with the phrase “one or more.” Where onlyone item is intended, the terms “one,” “single,” “only,” or similarlanguage is used. Further, the phrase “based on” is intended to mean“based, at least in part, on” unless explicitly stated otherwise.

What is claimed is:
 1. A device, comprising: one or more processorsconfigured to: identify a measure of expected interference associatedwith a particular sector of a radio access network (“RAN”) of a wirelessnetwork; identify a measure of actual interference associated with theparticular sector of the RAN of the wireless network; select aparticular interference model, from a plurality of interference models,based on a difference between the measure of expected interference andthe measure of actual interference associated with the particular sectorof the RAN of the wireless network, wherein each interference model ofthe plurality of interference models is associated with a respective setof actions; determine a particular set of actions associated with theselected particular interference model; and implement the determinedparticular set of actions with respect to the particular sector of theRAN of the wireless network.
 2. The device of claim 1, wherein the oneor more processors are further configured to: receive one or moreattributes of the particular sector of the RAN of the wireless network,wherein selecting the particular interference model from the pluralityof interference models is further based on the one or more attributes ofthe particular sector of the RAN of the wireless network.
 3. The deviceof claim 2, wherein the particular sector is a first sector, wherein theparticular set of actions is a first set of actions, and wherein the oneor more processors are further configured to: determine that a secondsector is associated with the same particular interference model;determine that the second sector is associated with one or moreattributes that are different from the one or more attributes associatedwith the first sector; and determine a second set of actions associatedwith the second sector based on the particular interference model andthe different attributes associated with the second sector.
 4. Thedevice of claim 1, wherein the particular sector is a first sector,wherein the measure of actual interference associated with the firstsector is a first measure of interference, wherein the one or moreprocessors are further configured to: identify a second measure ofinterference associated with a second sector concurrently with the firstmeasure of interference identified with respect to the first sector; andselect, based on attributes of the first sector and the second sector,the first sector to implement the determined set of actions.
 5. Thedevice of claim 4, wherein selecting the first sector includes forgoingimplementing one or more actions, based on the identified second measureof interference associated with the second sector, at wireless networkinfrastructure of the second sector.
 6. The device of claim 1, whereinthe measure of actual interference is based on one or more measurementreports generated by one or more User Equipment (“UEs”) located withinthe particular sector.
 7. The device of claim 6, wherein at least aparticular measurement report, of the one or more measurement reports,indicates: a detection of wireless signals from wireless networkinfrastructure of the particular sector, and a concurrent detection ofwireless signals from wireless network infrastructure of a differentsector.
 8. A non-transitory computer-readable medium, storing aplurality of processor-executable instructions to: identify a measure ofexpected interference associated with a particular sector of a radioaccess network (“RAN”) of a wireless network; identify a measure ofactual interference associated with the particular sector of the RAN ofthe wireless network; select a particular interference model, from aplurality of interference models, based on a difference between themeasure of expected interference and the measure of actual interferenceassociated with the particular sector of the RAN of the wirelessnetwork, wherein each interference model of the plurality ofinterference models is associated with a respective set of actions;determine a particular set of actions associated with the selectedparticular interference model; and implement the determined particularset of actions with respect to the particular sector of the RAN of thewireless network.
 9. The non-transitory computer-readable medium ofclaim 8, wherein the plurality of processor-executable instructionsfurther include processor-executable instructions to: receive one ormore attributes of the particular sector of the RAN of the wirelessnetwork, wherein selecting the particular interference model from theplurality of interference models is further based on the one or moreattributes of the particular sector of the RAN of the wireless network.10. The non-transitory computer-readable medium of claim 9, wherein theparticular sector is a first sector, wherein the particular set ofactions is a first set of actions, and wherein the plurality ofprocessor-executable instructions further include processor-executableinstructions to: determine that a second sector is associated with thesame particular interference model; determine that the second sector isassociated with one or more attributes that are different from the oneor more attributes associated with the first sector; and determine asecond set of actions associated with the second sector based on theparticular interference model and the different attributes associatedwith the second sector.
 11. The non-transitory computer-readable mediumof claim 8, wherein the particular sector is a first sector, wherein themeasure of actual interference associated with the first sector is afirst measure of interference, wherein the plurality ofprocessor-executable instructions further include processor-executableinstructions to: identify a second measure of interference associatedwith a second sector concurrently with the first measure of interferenceidentified with respect to the first sector; and select, based onattributes of the first sector and the second sector, the first sectorto implement the determined set of actions.
 12. The non-transitorycomputer-readable medium of claim 11, wherein selecting the first sectorincludes forgoing implementing one or more actions, based on theidentified second measure of interference associated with the secondsector, at wireless network infrastructure of the second sector.
 13. Thenon-transitory computer-readable medium of claim 8, wherein the measureof actual interference is based on one or more measurement reportsgenerated by one or more User Equipment (“UEs”) located within theparticular sector.
 14. The non-transitory computer-readable medium ofclaim 13, wherein at least a particular measurement report, of the oneor more measurement reports, indicates: a detection of wireless signalsfrom wireless network infrastructure of the particular sector, and aconcurrent detection of wireless signals from wireless networkinfrastructure of a different sector.
 15. A method, comprising:identifying a measure of expected interference associated with aparticular sector of a radio access network (“RAN”) of a wirelessnetwork; identifying a measure of actual interference associated withthe particular sector of the RAN of the wireless network; selecting aparticular interference model, from a plurality of interference models,based on a difference between the measure of expected interference andthe measure of actual interference associated with the particular sectorof the RAN of the wireless network, wherein each interference model ofthe plurality of interference models is associated with a respective setof actions; determining a particular set of actions associated with theselected particular interference model; and implementing the determinedparticular set of actions with respect to the particular sector of theRAN of the wireless network.
 16. The method of claim 15, furthercomprising: receiving one or more attributes of the particular sector ofthe RAN of the wireless network, wherein selecting the particularinterference model from the plurality of interference models is furtherbased on the one or more attributes of the particular sector of the RANof the wireless network.
 17. The method of claim 16, wherein theparticular sector is a first sector, wherein the particular set ofactions is a first set of actions, the method further comprising:determining that a second sector is associated with the same particularinterference model; determining that the second sector is associatedwith one or more attributes that are different from the one or moreattributes associated with the first sector; and determining a secondset of actions associated with the second sector based on the particularinterference model and the different attributes associated with thesecond sector.
 18. The method of claim 15, wherein the particular sectoris a first sector, wherein the measure of actual interference associatedwith the first sector is a first measure of interference, the methodfurther comprising: identifying a second measure of interferenceassociated with a second sector concurrently with the first measure ofinterference identified with respect to the first sector; and selecting,based on attributes of the first sector and the second sector, the firstsector to implement the determined set of actions, wherein selecting thefirst sector includes forgoing implementing one or more actions, basedon the identified second measure of interference associated with thesecond sector, at wireless network infrastructure of the second sector.19. The method of claim 15, wherein the measure of actual interferenceis based on one or more measurement reports generated by one or moreUser Equipment (“UEs”) located within the particular sector.
 20. Themethod of claim 19, wherein at least a particular measurement report, ofthe one or more measurement reports, indicates: a detection of wirelesssignals from wireless network infrastructure of the particular sector,and a concurrent detection of wireless signals from wireless networkinfrastructure of a different sector.